Posts Tagged ‘ teaching ’

Interactive visualizations of sampling and GP regression

December 9, 2017
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You really don’t want to miss Chi Feng‘s absolutely wonderful interactive demos. (1) Markov chain Monte Carlo sampling I believe this is exactly what Andrew was asking for a few Stan meetings ago: Chi Feng’s Interactive MCMC Sampling Visualizer This tool lets you explore a range of sampling algorithms including random-walk Metropolis, Hamiltonian Monte Carlo, […] The post Interactive visualizations of sampling and GP regression appeared first on Statistical Modeling,…

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Mind the gap

December 1, 2017
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Mind the gap

Teach the students you have Our job as teachers at any level is to teach the students we have. I embrace this idea from Dr Kevin Maxwell: “Our job is to teach the students we have. Not the ones we … Continue reading →

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Stan is a probabilistic programming language

November 23, 2017
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See here: Stan: A Probabilistic Programming Language. Journal of Statistical Software. (Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, Allen Riddell) And here: Stan is Turing Complete. So what? (Bob Carpenter) And, the pre-stan version: Fully Bayesian computing. (Jouni Kerman and Andrew Gelman) Apparently […] The post Stan is a probabilistic programming language appeared first on Statistical Modeling, Causal…

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Tips when conveying your research to policymakers and the news media

November 17, 2017
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Following up on a conversation regarding publicizing scientific research, Jim Savage wrote: Here’s a report that we produced a few years ago on prioritising potential policy levers to address the structural budget deficit in Australia. In the report we hid all the statistical analysis, aiming at an audience that would feel comfortable reading a broadsheet […] The post Tips when conveying your research to policymakers and the news media appeared…

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My talk tomorrow (Fri) 10am at Columbia

November 16, 2017
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I’m speaking for the statistics undergraduates tomorrow (Fri 17 Nov) 10am in room 312 Mathematics Bldg. I’m not quite sure what I’ll talk about: maybe I’ll do again my talk on statistics and sports, maybe I’ll speak on the statistical crisis in science. Anyone can come; especially we’d like to attract undergraduates—not just statistics majors—to […] The post My talk tomorrow (Fri) 10am at Columbia appeared first on Statistical Modeling,…

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Visualizing classifier thresholds

November 13, 2017
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Visualizing classifier thresholds

Lately I’ve been thinking a lot about the connection between prediction models and the decisions that they influence. There is a lot of theory around this, but communicating how the various pieces all fit together with the folks who will use and be impacted by these decisions can be challenging. One of the important conceptual pieces […]

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Looking for data on speed and traffic accidents—and other examples of data that can be fit by nonlinear models

November 2, 2017
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[cat picture] For the chapter in Regression and Other Stories that includes nonlinear regression, I’d like a couple homework problems where the kids have to construct and fit models to real data. So I need some examples. We already have the success of golf putts as a function of distance from the hole, and I’d […] The post Looking for data on speed and traffic accidents—and other examples of data…

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Advice for science writers!

October 28, 2017
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I spoke today at a meeting of science journalists, in a session organized by Betsy Mason, also featuring Kristin Sainani, Christie Aschwanden, and Tom Siegfried. My talk was on statistical paradoxes of science and science journalism, and I mentioned the Ted Talk paradox, Who watches the watchmen, the Eureka bias, the “What does not kill […] The post Advice for science writers! appeared first on Statistical Modeling, Causal Inference, and…

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My favorite definition of statistical significance

October 28, 2017
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From my 2009 paper with Weakliem: Throughout, we use the term statistically significant in the conventional way, to mean that an estimate is at least two standard errors away from some “null hypothesis” or prespecified value that would indicate no effect present. An estimate is statistically insignificant if the observed value could reasonably be explained […] The post My favorite definition of statistical significance appeared first on Statistical Modeling, Causal…

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Why I think the top batting average will be higher than .311: Over-pooling of point predictions in Bayesian inference

October 19, 2017
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In a post from 22 May 2017 entitled, “Who is Going to Win the Batting Crown?”, Jim Albert writes: At this point in the season, folks are interested in extreme stats and want to predict final season measures. On the morning of Saturday May 20, here are the leading batting averages: Justin Turner .379 Ryan […] The post Why I think the top batting average will be higher than .311:…

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